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Cross-validation and non-parametric k nearest-neighbour estimation

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  • Desheng Ouyang
  • Dong Li
  • Qi Li
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Abstract

In this paper we consider the problem of estimating a non-parametric regression function using the k nearest-neighbour method. We provide asymptotic theories for the least-squares cross validation (CV) selected smoothing parameter k for both local constant and local linear estimation methods. We also establish the asymptotic normality results for the resulting non-parametric regression function estimators. Some limited Monte Carlo experiments show that the CV method performs well in finite sample applications. Copyright Royal Economic Society 2006

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File URL: http://www.blackwell-synergy.com/doi/abs/10.1111/j.1368-423X.2006.00193.x
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Bibliographic Info

Article provided by Royal Economic Society in its journal Econometrics Journal.

Volume (Year): 9 (2006)
Issue (Month): 3 (November)
Pages: 448-471

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Handle: RePEc:ect:emjrnl:v:9:y:2006:i:3:p:448-471

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Cited by:
  1. repec:hal:journl:halshs-00423871 is not listed on IDEAS
  2. David Jacho-Chávez, 2008. "k nearest-neighbor estimation of inverse density weighted expectations," Economics Bulletin, AccessEcon, vol. 3(48), pages 1-6.
  3. repec:ebl:ecbull:v:3:y:2008:i:48:p:1-6 is not listed on IDEAS
  4. repec:hal:journl:halshs-00511979 is not listed on IDEAS
  5. repec:hal:journl:halshs-00505165 is not listed on IDEAS

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